Comprehensive Treatment of Hematological Patients with SARS-CoV-2 Infection Including Anti-SARS-CoV-2 Monoclonal Antibodies: A Single-Center Experience Case Series
Bibliographic record
Abstract
Patients with hematologic malignancies are at high risk of exacerbated condition and higher mortality from coronavirus disease 2019 (COVID-19). Bamlanivimab, casirivimab/imdevimab combination, and sotrovimab are monoclonal antibodies (mABs) that can reduce the risk of COVID-19-related hospitalization. Clinical effectiveness of bamlanivimab and casirivimab/imdevimab combination has been shown for the Delta variant (B.1.617.2), but the effectiveness of the latter treatment against the Omicron variant (B.1.1.529) has been suggested to be reduced. However, the tolerability and clinical usage of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)-specific mABs in patients with hematologic malignancies are less specified. We present a retrospective case series analysis of all SARS-CoV-2-infected patients with hematologic malignancies who received SARS-CoV-2-specific mABs at our facility between February and mid-December 2021. A total of 13 COVID-19 patients (pts) with at least one malignant hematologic diagnosis received SARS-CoV-2-specific mABs at our facility, with 3 pts receiving bamlanivimab and 10 pts receiving casirivimab/imdevimab combination. We observed SARS-CoV-2 clearance in five cases. Furthermore, we observed a reduction in the necessity for oxygen supplementation in five cases where the application was administered off-label. To the best of our knowledge, we present the largest collection of anecdotal cases of SARS-CoV-2-specific monoclonal antibody use in patients with hematological malignancies. Potential benefit of mABs may be reduced duration and/or clearance of persistent SARS-CoV-2 infection.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".